Motivation

IMAGE ALT TEXT HERE

Data wrangling

Loading the gapminder and dplyr packages

# Load the gapminder package
library(gapminder)

# Load the dplyr package
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
# Look at the gapminder dataset
gapminder

Understanding a data frame

# How many observations (rows) are in the dataset?
nrow(gapminder)
## [1] 1704

The filter verb

Filtering for one year

# Filter the gapminder dataset for the year 1957
gapminder %>% filter(year==1957)

Filtering for one country and one year

# Filter for China in 2002
gapminder %>% filter(country=="China",year==2002)

The arrange verb

Arranging observations by life expectancy

# Sort in ascending order of lifeExp
gapminder %>% arrange(lifeExp)
# Sort in descending order of lifeExp
gapminder %>% arrange(desc(lifeExp))

Filtering and arranging

# Filter for the year 1957, then arrange in descending order of population
gapminder %>% filter(year==1957)%>%arrange(desc(pop))

The mutate verb

Using mutate to change or create a column

# Use mutate to change lifeExp to be in months
gapminder %>% mutate(lifeExp = 12 * lifeExp)
# Use mutate to create a new column called lifeExpMonths
gapminder %>% mutate(lifeExpMonths = 12 * lifeExp)

Combining filter, mutate, and arrange

# Filter, mutate, and arrange the gapminder dataset
gapminder %>% 
filter(year==2007) %>%
mutate(lifeExpMonths = 12 * lifeExp) %>%
arrange(desc(lifeExpMonths))

Data visualization

Variable assignment

# Load the ggplot2 package as well
library(ggplot2)

# Create gapminder_1952
gapminder_1952 <- gapminder %>% filter(year==1952)

Comparing population and GDP per capita

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Change to put pop on the x-axis and gdpPercap on the y-axis
ggplot(gapminder_1952, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

# Change to put pop on the x-axis and gdpPercap on the y-axis
ggplot(gapminder_1952, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) +
  geom_point()

Comparing population and life expectancy

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a scatter plot with pop on the x-axis and lifeExp on the y-axis
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
  geom_point()

Log Scales

Putting the x-axis on a log scale

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Change this plot to put the x-axis on a log scale
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
  geom_point()+scale_x_log10()

Putting the x- and y- axes on a log scale

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Scatter plot comparing pop and gdpPercap, with both axes on a log scale
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) +
  geom_point()+scale_x_log10() +
  scale_y_log10()

Adding color to a scatter plot

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Scatter plot comparing pop and lifeExp, with color representing continent
ggplot(gapminder_1952, aes(x = pop, y = lifeExp,color=continent)) +
  geom_point()+scale_x_log10() +
  scale_y_log10()

Adding size and color to a plot

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Add the size aesthetic to represent a country's gdpPercap
ggplot(gapminder_1952, aes(x = pop, y = lifeExp, color = continent,size=gdpPercap)) +
  geom_point() +
  scale_x_log10()

Faceting

Creating a subgraph for each continent

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Scatter plot comparing pop and lifeExp, faceted by continent
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
  geom_point() +
  scale_x_log10()+
  facet_wrap(~ continent)

Faceting by year

# Scatter plot comparing gdpPercap and lifeExp, with color representing continent
# and size representing population, faceted by year
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp,color=continent,size=pop)) +
  geom_point() +
  scale_x_log10()+
  facet_wrap(~ year)

Grouping and summarizing

Summarizing the median life expectancy

# Summarize to find the median life expectancy
gapminder %>% summarise(medianLifeExp = median(lifeExp))

Summarizing the median life expectancy in 1957

# Filter for 1957 then summarize the median life expectancy
gapminder %>% 
filter(year==1957) %>%
summarise(medianLifeExp = median(lifeExp))

Summarizing multiple variables in 1957

# Filter for 1957 then summarize the median life expectancy and the maximum GDP per capita
gapminder %>% 
filter(year == 1957) %>%
summarise(medianLifeExp = median(lifeExp),
      maxGdpPercap = max(gdpPercap))

group_by verb

Summarizing by year

# Find median life expectancy and maximum GDP per capita in each year

gapminder %>% 
group_by(year) %>%
summarise(medianLifeExp = median(lifeExp),
      maxGdpPercap = max(gdpPercap))

Summarizing by continent

# Find median life expectancy and maximum GDP per capita in each continent in 1957

gapminder %>% 
filter(year == 1957) %>%
group_by(continent) %>%
summarise(medianLifeExp = median(lifeExp),
      maxGdpPercap = max(gdpPercap))

Summarizing by continent and year

# Find median life expectancy and maximum GDP per capita in each year/continent combination
gapminder %>% 
group_by(continent,year) %>%
summarise(medianLifeExp = median(lifeExp),
      maxGdpPercap = max(gdpPercap))

Visualizing summarized data

Visualizing median life expectancy over time

by_year <- gapminder %>%
  group_by(year) %>%
  summarize(medianLifeExp = median(lifeExp),
            maxGdpPercap = max(gdpPercap))

# Create a scatter plot showing the change in medianLifeExp over time
by_year %>% 
ggplot(aes(x=year,y=medianLifeExp))+
geom_point()+expand_limits(y = 0)

Visualizing median GDP per capita per continent over time

# Summarize medianGdpPercap within each continent within each year: by_year_continent
by_year_continent <- gapminder %>%
  group_by(continent, year) %>%
  summarize(medianGdpPercap = median(gdpPercap))

# Plot the change in medianGdpPercap in each continent over time
by_year_continent %>% 
ggplot(aes(x=year,y=medianGdpPercap,color=continent))+
geom_point()+expand_limits(y = 0)

Comparing median life expectancy and median GDP per continent in 2007

# Summarize the median GDP and median life expectancy per continent in 2007
by_continent_2007 <- gapminder %>%
  filter(year == 2007) %>%
  group_by(continent) %>% 
  summarize(medianLifeExp = median(lifeExp), 
  medianGdpPercap = median(gdpPercap))


# Use a scatter plot to compare the median GDP and median life expectancy
by_continent_2007 %>% 
ggplot(aes(x=medianGdpPercap,y=medianLifeExp,color=continent))+
geom_point()+expand_limits(y = 0)

Types of visualizations

Line plots

Visualizing median GDP per capita over time

# Summarize the median gdpPercap by year, then save it as by_year
by_year <- gapminder %>%
group_by(year)%>%
summarise(medianGdpPercap = median(gdpPercap))

# Create a line plot showing the change in medianGdpPercap over time
by_year %>% 
ggplot(aes(x=year,y=medianGdpPercap))+
geom_line()+expand_limits(y = 0)

Visualizing median GDP per capita by continent over time

# Summarize the median gdpPercap by year & continent, save as by_year_continent
by_year_continent <- gapminder %>%
group_by(continent, year)%>%
summarise(medianGdpPercap = median(gdpPercap))

# Create a line plot showing the change in medianGdpPercap by continent over time
by_year_continent %>% 
ggplot(aes(x=year,y=medianGdpPercap,color=continent))+
geom_line()+expand_limits(y = 0)

Bar Plots

Visualizing median GDP per capita by continent

# Summarize the median gdpPercap by year and continent in 1952
by_continent<- gapminder %>%
filter(year==1952) %>%
group_by(continent) %>%
summarise(medianGdpPercap = median(gdpPercap))


# Create a bar plot showing medianGdp by continent
by_continent %>% 
ggplot(aes(x=continent,y=medianGdpPercap))+
geom_col()

Visualizing GDP per capita by country in Oceania

# Filter for observations in the Oceania continent in 1952
oceania_1952<- gapminder %>%
filter(continent=="Oceania",year==1952)

# Create a bar plot of gdpPercap by country
oceania_1952 %>% 
ggplot(aes(x=country,y=gdpPercap))+
geom_col()

Histograms

Visualizing population

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a histogram of population (pop)
gapminder_1952 %>% 
ggplot(aes(x=pop))+
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Visualizing population with x-axis on a log scale

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a histogram of population (pop), with x on a log scale
gapminder_1952 %>% 
ggplot(aes(x=pop))+
geom_histogram()+
scale_x_log10()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Box Plots

Comparing GDP per capita across continents

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a boxplot comparing gdpPercap among continents
gapminder_1952 %>% 
ggplot(aes(x=continent,y=gdpPercap))+
geom_boxplot()+
scale_y_log10()

Adding a title to your graph

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Add a title to this graph: "Comparing GDP per capita across continents"
ggplot(gapminder_1952, aes(x = continent, y = gdpPercap)) +
  geom_boxplot() +
  scale_y_log10()+
  ggtitle("Comparing GDP per capita across continents")

Endgegner

# install.packages('devtools')
#devtools::install_github('thomasp85/gganimate')

library(gapminder)
library(gganimate)

ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')